Patient-Specific Detection of Atrial Fibrillation in Segments of ECG Signals using Deep Neural Networks

Abstract

<jats:p>Atrial Fibrillation (AF) is the most common cardiac arrhythmia worldwide. It is associated with reduced quality of life and increases the risk of stroke and myocardial infarction. Unfortunately, many cases of AF are asymptomatic and undiagnosed, which increases the risk for the patients. Due to its paroxysmal nature, the detection of AF requires the evaluation, by a cardiologist, of long-term ECG signals. In Colombia, it is difficult to have access to an early diagnosis of AF because of the associated costs to the detection and the geographical distribution of cardiologists. This work is part of a macro project that aims to develop a specific-patient portable device for the detection of AF. This device will be based on a Convolutional Neural Network (CNN). We aim to find a suitable CNN model, which later could be implemented in hardware. Diverse techniques were applied to improve the answer regarding accuracy, sensitivity, specificity, and precision. The final model achieves an accuracy of , a specificity of , a sensitivity of and a precision of . During the development of the model, the computational cost and memory resources were taking into account in order to obtain an efficient hardware model in a future implementation of the device.</jats:p>

A Computational Architecture for Inference of a Quantized-CNN for Detecting Atrial Fibrillation

“…In this paper, the CNN Castillo-Granados [14] is implemented ( Figure 1). This model was trained for the detection of AF from ECG signals.…”

“…These ECG signals were registered by the Einthoven triangle method [15] and stored in a vector of 500 samples with a sampling rate of 250 [samples/s]. This CNN achieved an accuracy of 97.44% using a 64-bit doublefloat format [14].…”

“…In this work, we propose a computational architecture for the inference process of a quantized version of the Castillo-Granados CNN [14]. Our goal is to design a specific purpose processor that carries out the inference process by using the minimum amount of computational and memory resources at high accuracy possible.…”

“…We designed a SIMD architecture (Single Instruction, Multiple Data) with a single vector unit that is optimized to perform both the convolution and fully connected layers. This processor allows the inference of a 22-bits Q-CNN version [14] and achieves a 94% accuracy.…”

A New Method for Refined Recognition for Heart Disease Diagnosis Based on Deep Learning

“…The main problems include the following: the accuracy of the ECG source signal needs to be improved; the algorithm recognition speed has limitations; the accuracy of the ECG signals recognition algorithm used in clinical practice is not high; the ECG signal has not only the specificity between individuals, but also the same individuals have great differences in different times and different physical conditions; there are human factors in signal acquisition or electromagnetic interference from the surrounding equipment. At present, most of the research studies on cardiac electrophysiological signals are based on one-dimensional signal models [21][22][23], which is very difficult not only to meet the classification task, but also to detect and locate the target quickly from the ECG information.…”